The authors propose Fund2Persona, a framework that grounds financial advisor personas in fund disclosures, holdings transitions, and manager commentary to address the difficulty of scaling consistent expertise in LLM systems. The system refines these personas through an agentic actor-scorer-patcher loop, moving beyond simple persona prompts that often drift toward generic recommendations.
- Fund2Persona constructs personas using fund disclosures, holdings transitions, market context, and manager commentary.
- It employs an agentic actor-scorer-patcher loop to refine the generated personas.
- The framework was evaluated on held-out holdings-transition reconstruction and manager-commentary alignment tasks.
- Downstream diagnostics included market-scenario generation and advisory dialogues grounded in investor profiles.
The results indicate that fund-data-grounded financial-advisor personas can make manager-specific investment expertise portable, providing more specific and useful advice than generic baselines.